Exoskeletons have been used for performance restoration and enhancement. Recently, the importance of the natural dynamics of the human body, energy input, and comfort of human-robot interactions have been given increased attention in exoskeleton applications. In these approaches to exoskeleton assistance torque control is crucial. In such systems, series-elastic actuators are commonly used to provide low error torque tracking in the presence of unknown and changing human dynamics.
It has been a common interest for the lower-limb exoskeleton community to improve locomotion performance. The ankle joint has drawn attention for effort reduction in walking since it produces more mechanical work than other joints. Better ankle joint torque tracking can improve exoskeleton control. Such techniques are also expected to extend to knee and hip exoskeletons, for which the control problem is similar.
Control of exoskeletons is normally hierarchical, with high level controllers determining behavior-related desired torques and torque control lying at a lower level. Torque controllers are called low-level controllers and desired torque generators are called high-level controllers. Many low-level control methods have been employed for torque or position tracking in exoskeletons, including classical feedback control, model-based control, adaptive control and iterative learning control. However, it remains unclear which method has the best performance, or how performance may vary with high-level controllers.
High-level controllers based on time, joint angle, neuromuscular models, and electromyographic measurements have been used to assist human walking. Each may be advantageous in some assistance paradigms, and each generates desired torques with different dynamics.
The topic of exoskeleton torque control has not drawn as much attention as high-level control and biomechanics outcomes. In cases where torque control has been addressed directly, it has typically been investigated under unrealistic conditions, i.e., during benchtop tests rather than human-robot interactions, and results have often not been reported quantitatively. Moreover, little has been reported on the relative performance of different torque controllers on the same platform, making differentiation among candidate methods difficult.